Along with continuous emergence of new types of sensors, the ability of people to obtain images is improved rapidly, and images generated by sensors of different physical characteristics are also increased continuously. Since image data obtained by different image sensors have obvious limitations and differences, merely using one kind of image data generally can hardly meet actual requirements. Therefore, there is a need to combine images obtained by different sensors via an image fusion technique to achieve more comprehensive, clearer and more accurate understanding and recognition of targets in the images. For example, in medical science, images obtained in different forms, e.g., computed tomography imaging (CT), magnetic resonance imaging (MRI), and ultrasound (US), by a comprehensive analysis of anatomic and physiological information are fused to realize an improved diagnosing process.
The image registration technique is an important precondition for achieving the image fusion, and is a problem to be firstly solved for the image fusion. The ICP (Iterative Closest Point) algorithm is a method for achieving a registration of images, and the ICP algorithm is a process of repeatedly performing “determination of a set of corresponding points, and calculation of an optimal rigid change” based on a registration method of a curved surface in a free form, until a preset registration convergence criteria is reached, the final coordinate change being a composition of respective changes. However, since the disadvantages that the initial deviation range of the registration target and the registration source cannot be too large, and although the convergence can be made to a local limit, a global optimization generally cannot be achieved (i.e., failing to get rid of the local limit) exist in the conventional ICP algorithm, accuracy and success rate of the image registration are comparatively low, which cannot meet actual requirements.
Upon a study of the prior art, the applicant finds that the existing image registration method based on the ICP algorithm generally employs two methods, i.e., “applying a random rigid perturbation transformation to a registration” and “applying a random perturbation to three-dimensional coordinate points of a registration target so that it is deformed”, to achieve the object of getting rid of the local limit of the conventional ICP algorithm to make the registration effect better. However, the employment of the method of “applying a random rigid perturbation transformation to a registration” requires sampling in a space with six-dimensional freedom, needs a large amount of calculations, and takes time. With respect to the employment of the method of “applying a random perturbation to three-dimensional coordinate points of a registration target to deform it”, the parameters thereof are set empirically in actual applications in accordance with different experimental models, and with regard to different experimental models, the setting of parameters is comparatively difficult, and a wrong setting is likely to result in an unsuccessful registration.